A peer review system that automatically evaluates student feedback comments was deployed in a university research methods course. The course required students to create an argument diagram to justify a hypothesis, then use this diagram to write a paper introduction. Diagram and paper first drafts were both reviewed by peers. During peer review, the system automatically analyzed the quality of student comments with respect to localization (i.e. pinpointing the source of the comment in the diagram or paper). Two localization models (one for diagram and one for paper reviews) triggered a system scaffolding intervention to improve review quality whenever the review was predicted to have a ratio of localized comments less than a threshold. Reviewers could then choose to revise their comments or ignore the scaffolding. Our analysis of data from system logs demonstrates that diagram and paper localization models have high prediction accuracy, and that a larger portion of student feedback comments are successfully localized after scaffolded revision.